Origin is building physical AI for the built world. Our robots autonomously finish building interiors at production quality. OG-1 is deployed on live NYC commercial construction sites today. Backed by Accel.
Our system runs a Multi Agent Action Expert architecture: classical precision algorithms orchestrated alongside learned policies. The job is systematically expanding the learned components while keeping the system production-safe.
The Role
You own the full lifecycle of learned components on OG-1: from data collection and model training through edge deployment on Jetson AGX Orin. Every research project will have a deployment milestone. This is not a lab position.
What You Will Do
• Train and deploy VLA models for contact-rich manipulation using our imitation learning infrastructure
• Build the data flywheel: teleoperation pipelines (GELLO, SpaceMouse, VR), DAgger-style online correction, demonstration curation
• Research and prototype world models for surface state prediction, spray dynamics, and anomaly detection
• Design offline evaluation metrics that predict real-world finishing quality before deployment
• Optimize models for edge: TensorRT compilation, latency profiling, memory budgeting on dual Jetson AGX Orin
• Hands-on experience with world models, including building or working with predictive or generative workplace models (e.g., latent dynamics, video prediction, or planning-oriented models)
• Design the interface where learned policies propose actions and deterministic safety layers enforce constraints
Requirements
• BS/MS/PhD in CS, Robotics, ML, or equivalent experience shipping learned systems on physical robots
• Strong Python and PyTorch; comfort modifying research codebases (you'll work directly with open-source VLA implementations)
• Experience in at least two of: imitation learning, RL, vision-language models, robot learning from demonstration, sim-to-real
• Track record deploying ML on real hardware: not just training to